{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a09a9870",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 读取数据\n",
    "df = pd.read_csv(r\"E:\\机器学习房价预测\\高分回归\\comparison_results\\rank_and_score.csv\")\n",
    "\n",
    "# === 条形图部分 ===\n",
    "sns.set(style=\"whitegrid\", font_scale=1.2)\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "metrics = [\"RMSE\", \"MAE\", \"R^2\", \"Score\", \"Train Time (s)\"]\n",
    "plot_titles = {\n",
    "    \"RMSE\": \"Root Mean Squared Error (Lower is Better)\",\n",
    "    \"MAE\": \"Mean Absolute Error (Lower is Better)\",\n",
    "    \"R^2\": \"R² Score (Higher is Better)\",\n",
    "    \"Score\": \"Custom Score (Lower is Better)\",\n",
    "    \"Train Time (s)\": \"Training Time (Seconds)\"\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, metric in enumerate(metrics):\n",
    "    ax = axes[i]\n",
    "    ascending = metric != \"R^2\"  # 除了R^2，其它指标越小越好\n",
    "    sorted_df = df.sort_values(metric, ascending=ascending)\n",
    "\n",
    "    sns.barplot(data=sorted_df, x=metric, y=\"Model\", ax=ax, palette=\"coolwarm\")\n",
    "    ax.set_title(plot_titles[metric])\n",
    "    ax.set_xlabel(metric)\n",
    "    ax.set_ylabel(\"Model\")\n",
    "    for j, value in enumerate(sorted_df[metric]):\n",
    "        offset = value * 0.01 if value > 1 else 0.5\n",
    "        ax.text(value + offset if ascending else value - offset,\n",
    "                j, f\"{value:.2f}\", va='center', fontsize=9)\n",
    "\n",
    "# 删除多余子图\n",
    "if len(metrics) < len(axes):\n",
    "    for j in range(len(metrics), len(axes)):\n",
    "        fig.delaxes(axes[j])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"bar_comparison_all_metrics.pdf\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# === 雷达图部分 ===\n",
    "radar_metrics = [\"RMSE\", \"MAE\", \"R^2\", \"Score\", \"Train Time (s)\"]\n",
    "data_radar = df[[\"Model\"] + radar_metrics].copy()\n",
    "data_radar.set_index(\"Model\", inplace=True)\n",
    "\n",
    "# 反向处理非R^2指标\n",
    "for col in radar_metrics:\n",
    "    if col != \"R^2\":\n",
    "        data_radar[col] = data_radar[col].max() - data_radar[col]\n",
    "\n",
    "# 归一化\n",
    "scaler = MinMaxScaler()\n",
    "data_normalized = pd.DataFrame(scaler.fit_transform(data_radar),\n",
    "                               index=data_radar.index,\n",
    "                               columns=radar_metrics)\n",
    "\n",
    "# 雷达图准备\n",
    "labels = radar_metrics\n",
    "angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()\n",
    "angles += angles[:1]\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "for model_name in data_normalized.index:\n",
    "    values = data_normalized.loc[model_name].tolist()\n",
    "    values += values[:1]\n",
    "    plt.polar(angles, values, label=model_name, linewidth=2)\n",
    "    plt.fill(angles, values, alpha=0.1)\n",
    "\n",
    "plt.xticks(angles[:-1], labels)\n",
    "plt.yticks([0.25, 0.5, 0.75], [\"0.25\", \"0.5\", \"0.75\"], color=\"gray\", size=10)\n",
    "plt.title(\"Model Performance Radar Chart (Normalized)\", size=14)\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"radar_comparison.pdf\", dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a93caa6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 读取数据\n",
    "df = pd.read_csv(r\"E:\\机器学习房价预测\\高分回归\\comparison_results\\rank_and_score.csv\")\n",
    "# 图表美观设置\n",
    "sns.set(style=\"whitegrid\", font_scale=1.2)\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "# 指标和标题设置\n",
    "metrics = [\"RMSE\", \"MAE\", \"R^2\", \"Score\", \"Train Time (s)\"]\n",
    "plot_titles = {\n",
    "    \"RMSE\": \"Root Mean Squared Error (Lower is Better)\",\n",
    "    \"MAE\": \"Mean Absolute Error (Lower is Better)\",\n",
    "    \"R^2\": \"R² Score (Higher is Better)\",\n",
    "    \"Score\": \"Custom Score (Lower is Better)\",\n",
    "    \"Train Time (s)\": \"Training Time (Seconds)\"\n",
    "}\n",
    "\n",
    "# 分别绘制每个条形图\n",
    "for metric in metrics:\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    ascending = metric != \"R^2\"\n",
    "    sorted_df = df.sort_values(metric, ascending=ascending)\n",
    "\n",
    "    ax = sns.barplot(data=sorted_df, x=metric, y=\"Model\", palette=\"crest\")\n",
    "    plt.title(plot_titles[metric])\n",
    "    plt.xlabel(metric)\n",
    "    plt.ylabel(\"Model\")\n",
    "\n",
    "    for j, value in enumerate(sorted_df[metric]):\n",
    "        offset = value * 0.01 if value > 1 else 0.5\n",
    "        ax.text(value + offset if ascending else value - offset,\n",
    "                j, f\"{value:.2f}\", va='center', fontsize=10)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"{metric}_barplot.png\", dpi=300)  # 或改为 .png\n",
    "    plt.show()\n",
    "\n",
    "# === 雷达图绘制 ===\n",
    "radar_metrics = [\"RMSE\", \"MAE\", \"R^2\", \"Score\", \"Train Time (s)\"]\n",
    "data_radar = df[[\"Model\"] + radar_metrics].copy()\n",
    "data_radar.set_index(\"Model\", inplace=True)\n",
    "\n",
    "# 非R^2指标反转\n",
    "for col in radar_metrics:\n",
    "    if col != \"R^2\":\n",
    "        data_radar[col] = data_radar[col].max() - data_radar[col]\n",
    "\n",
    "# 归一化\n",
    "scaler = MinMaxScaler()\n",
    "data_normalized = pd.DataFrame(scaler.fit_transform(data_radar),\n",
    "                               index=data_radar.index,\n",
    "                               columns=radar_metrics)\n",
    "\n",
    "# 雷达图准备\n",
    "labels = radar_metrics\n",
    "angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()\n",
    "angles += angles[:1]\n",
    "\n",
    "# 绘制雷达图\n",
    "plt.figure(figsize=(8, 8))\n",
    "for model_name in data_normalized.index:\n",
    "    values = data_normalized.loc[model_name].tolist()\n",
    "    values += values[:1]\n",
    "    plt.polar(angles, values, label=model_name, linewidth=2)\n",
    "    plt.fill(angles, values, alpha=0.1)\n",
    "\n",
    "plt.xticks(angles[:-1], labels)\n",
    "plt.yticks([0.25, 0.5, 0.75], [\"0.25\", \"0.5\", \"0.75\"], color=\"gray\", size=10)\n",
    "plt.title(\"Model Performance Radar Chart (Normalized)\", size=14)\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Radar_Chart.pdf\", dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38710eb0",
   "metadata": {},
   "source": [
    "#### 新"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df1f82b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a75c570",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"E:\\机器学习房价预测\\高分回归\\comparison_results\\rank_and_score.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf501604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Model",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Type",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Hyperparameters",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Train Time (s)",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "RMSE",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "MAE",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "R^2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Score",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Rank_add_self",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Rank_leaderboard",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "609dcb17-5628-4c8e-b4b9-ff0cc1a7f3b0",
       "rows": [
        [
         "0",
         "XGBoost",
         "Tuned",
         "{'n_estimators': 500, 'max_depth': 7, 'learning_rate': 0.1}",
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         "15388.65",
         "7198.2",
         "0.9616",
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         "1",
         "GradientBoosting",
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         "8.185",
         "14971.76",
         "7991.69",
         "0.9637",
         "0.05161",
         "80",
         "79"
        ],
        [
         "2",
         "LightGBM",
         "Tuned",
         "{'n_estimators': 500, 'learning_rate': 0.1, 'num_leaves': 30}",
         "0.292",
         "16667.83",
         "8054.88",
         "0.955",
         "0.05376",
         "81",
         "79"
        ],
        [
         "3",
         "RandomForest",
         "Tuned",
         "{'n_estimators': 500, 'max_depth': 10}",
         "10.826",
         "21910.33",
         "12267.13",
         "0.9222",
         "0.08547",
         "98",
         "95"
        ],
        [
         "4",
         "KNeighbors",
         "Tuned",
         "{'n_neighbors': 3, 'weights': 'distance'}",
         "0.002",
         "31176.21",
         "14677.41",
         "0.8426",
         "0.09667",
         "105",
         "101"
        ],
        [
         "5",
         "LinearRegression",
         "Default",
         "Default",
         "0.033",
         "88167.05",
         "17192.36",
         "-0.2592",
         "0.13401",
         "1629",
         "1624"
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        [
         "6",
         "Ridge",
         "Tuned",
         "{'alpha': 0.1}",
         "0.003",
         "88184.92",
         "17186.57",
         "-0.2597",
         "0.13401",
         "1629",
         "1624"
        ],
        [
         "7",
         "ElasticNet",
         "Tuned",
         "{'alpha': 0.1, 'l1_ratio': 0.0}",
         "0.179",
         "105148.23",
         "18837.56",
         "-0.791",
         "0.14246",
         "2339",
         "2333"
        ],
        [
         "8",
         "AdaBoost",
         "Tuned",
         "{'n_estimators': 300, 'learning_rate': 0.1}",
         "2.77",
         "31977.2",
         "21568.23",
         "0.8344",
         "0.15272",
         "3287",
         "3280"
        ],
        [
         "9",
         "Lasso",
         "Tuned",
         "{'alpha': 0.1}",
         "0.01",
         "247505.43",
         "34986.2",
         "-8.9232",
         "0.21209",
         "4098",
         "4091"
        ]
       ],
       "shape": {
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        "rows": 10
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      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Type</th>\n",
       "      <th>Hyperparameters</th>\n",
       "      <th>Train Time (s)</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R^2</th>\n",
       "      <th>Score</th>\n",
       "      <th>Rank_add_self</th>\n",
       "      <th>Rank_leaderboard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'n_estimators': 500, 'max_depth': 7, 'learnin...</td>\n",
       "      <td>1.278</td>\n",
       "      <td>15388.65</td>\n",
       "      <td>7198.20</td>\n",
       "      <td>0.9616</td>\n",
       "      <td>0.05123</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'n_estimators': 500, 'learning_rate': 0.1, 'm...</td>\n",
       "      <td>8.185</td>\n",
       "      <td>14971.76</td>\n",
       "      <td>7991.69</td>\n",
       "      <td>0.9637</td>\n",
       "      <td>0.05161</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'n_estimators': 500, 'learning_rate': 0.1, 'n...</td>\n",
       "      <td>0.292</td>\n",
       "      <td>16667.83</td>\n",
       "      <td>8054.88</td>\n",
       "      <td>0.9550</td>\n",
       "      <td>0.05376</td>\n",
       "      <td>81</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'n_estimators': 500, 'max_depth': 10}</td>\n",
       "      <td>10.826</td>\n",
       "      <td>21910.33</td>\n",
       "      <td>12267.13</td>\n",
       "      <td>0.9222</td>\n",
       "      <td>0.08547</td>\n",
       "      <td>98</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'n_neighbors': 3, 'weights': 'distance'}</td>\n",
       "      <td>0.002</td>\n",
       "      <td>31176.21</td>\n",
       "      <td>14677.41</td>\n",
       "      <td>0.8426</td>\n",
       "      <td>0.09667</td>\n",
       "      <td>105</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>Default</td>\n",
       "      <td>Default</td>\n",
       "      <td>0.033</td>\n",
       "      <td>88167.05</td>\n",
       "      <td>17192.36</td>\n",
       "      <td>-0.2592</td>\n",
       "      <td>0.13401</td>\n",
       "      <td>1629</td>\n",
       "      <td>1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'alpha': 0.1}</td>\n",
       "      <td>0.003</td>\n",
       "      <td>88184.92</td>\n",
       "      <td>17186.57</td>\n",
       "      <td>-0.2597</td>\n",
       "      <td>0.13401</td>\n",
       "      <td>1629</td>\n",
       "      <td>1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'alpha': 0.1, 'l1_ratio': 0.0}</td>\n",
       "      <td>0.179</td>\n",
       "      <td>105148.23</td>\n",
       "      <td>18837.56</td>\n",
       "      <td>-0.7910</td>\n",
       "      <td>0.14246</td>\n",
       "      <td>2339</td>\n",
       "      <td>2333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'n_estimators': 300, 'learning_rate': 0.1}</td>\n",
       "      <td>2.770</td>\n",
       "      <td>31977.20</td>\n",
       "      <td>21568.23</td>\n",
       "      <td>0.8344</td>\n",
       "      <td>0.15272</td>\n",
       "      <td>3287</td>\n",
       "      <td>3280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>Tuned</td>\n",
       "      <td>{'alpha': 0.1}</td>\n",
       "      <td>0.010</td>\n",
       "      <td>247505.43</td>\n",
       "      <td>34986.20</td>\n",
       "      <td>-8.9232</td>\n",
       "      <td>0.21209</td>\n",
       "      <td>4098</td>\n",
       "      <td>4091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model     Type  \\\n",
       "0           XGBoost    Tuned   \n",
       "1  GradientBoosting    Tuned   \n",
       "2          LightGBM    Tuned   \n",
       "3      RandomForest    Tuned   \n",
       "4        KNeighbors    Tuned   \n",
       "5  LinearRegression  Default   \n",
       "6             Ridge    Tuned   \n",
       "7        ElasticNet    Tuned   \n",
       "8          AdaBoost    Tuned   \n",
       "9             Lasso    Tuned   \n",
       "\n",
       "                                     Hyperparameters  Train Time (s)  \\\n",
       "0  {'n_estimators': 500, 'max_depth': 7, 'learnin...           1.278   \n",
       "1  {'n_estimators': 500, 'learning_rate': 0.1, 'm...           8.185   \n",
       "2  {'n_estimators': 500, 'learning_rate': 0.1, 'n...           0.292   \n",
       "3             {'n_estimators': 500, 'max_depth': 10}          10.826   \n",
       "4          {'n_neighbors': 3, 'weights': 'distance'}           0.002   \n",
       "5                                            Default           0.033   \n",
       "6                                     {'alpha': 0.1}           0.003   \n",
       "7                    {'alpha': 0.1, 'l1_ratio': 0.0}           0.179   \n",
       "8        {'n_estimators': 300, 'learning_rate': 0.1}           2.770   \n",
       "9                                     {'alpha': 0.1}           0.010   \n",
       "\n",
       "        RMSE       MAE     R^2    Score  Rank_add_self  Rank_leaderboard  \n",
       "0   15388.65   7198.20  0.9616  0.05123             79                79  \n",
       "1   14971.76   7991.69  0.9637  0.05161             80                79  \n",
       "2   16667.83   8054.88  0.9550  0.05376             81                79  \n",
       "3   21910.33  12267.13  0.9222  0.08547             98                95  \n",
       "4   31176.21  14677.41  0.8426  0.09667            105               101  \n",
       "5   88167.05  17192.36 -0.2592  0.13401           1629              1624  \n",
       "6   88184.92  17186.57 -0.2597  0.13401           1629              1624  \n",
       "7  105148.23  18837.56 -0.7910  0.14246           2339              2333  \n",
       "8   31977.20  21568.23  0.8344  0.15272           3287              3280  \n",
       "9  247505.43  34986.20 -8.9232  0.21209           4098              4091  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08b697dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
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        {
         "name": "Train Time (s)",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "RMSE",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "MAE",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "R^2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Score",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Rank_add_self",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Rank_leaderboard",
         "rawType": "int64",
         "type": "integer"
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       "shape": {
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Time (s)</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R^2</th>\n",
       "      <th>Score</th>\n",
       "      <th>Rank_add_self</th>\n",
       "      <th>Rank_leaderboard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>1.278</td>\n",
       "      <td>15388.65</td>\n",
       "      <td>7198.20</td>\n",
       "      <td>0.9616</td>\n",
       "      <td>0.05123</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>8.185</td>\n",
       "      <td>14971.76</td>\n",
       "      <td>7991.69</td>\n",
       "      <td>0.9637</td>\n",
       "      <td>0.05161</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.292</td>\n",
       "      <td>16667.83</td>\n",
       "      <td>8054.88</td>\n",
       "      <td>0.9550</td>\n",
       "      <td>0.05376</td>\n",
       "      <td>81</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>10.826</td>\n",
       "      <td>21910.33</td>\n",
       "      <td>12267.13</td>\n",
       "      <td>0.9222</td>\n",
       "      <td>0.08547</td>\n",
       "      <td>98</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>0.002</td>\n",
       "      <td>31176.21</td>\n",
       "      <td>14677.41</td>\n",
       "      <td>0.8426</td>\n",
       "      <td>0.09667</td>\n",
       "      <td>105</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.033</td>\n",
       "      <td>88167.05</td>\n",
       "      <td>17192.36</td>\n",
       "      <td>-0.2592</td>\n",
       "      <td>0.13401</td>\n",
       "      <td>1629</td>\n",
       "      <td>1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.003</td>\n",
       "      <td>88184.92</td>\n",
       "      <td>17186.57</td>\n",
       "      <td>-0.2597</td>\n",
       "      <td>0.13401</td>\n",
       "      <td>1629</td>\n",
       "      <td>1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>0.179</td>\n",
       "      <td>105148.23</td>\n",
       "      <td>18837.56</td>\n",
       "      <td>-0.7910</td>\n",
       "      <td>0.14246</td>\n",
       "      <td>2339</td>\n",
       "      <td>2333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>2.770</td>\n",
       "      <td>31977.20</td>\n",
       "      <td>21568.23</td>\n",
       "      <td>0.8344</td>\n",
       "      <td>0.15272</td>\n",
       "      <td>3287</td>\n",
       "      <td>3280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.010</td>\n",
       "      <td>247505.43</td>\n",
       "      <td>34986.20</td>\n",
       "      <td>-8.9232</td>\n",
       "      <td>0.21209</td>\n",
       "      <td>4098</td>\n",
       "      <td>4091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model  Train Time (s)       RMSE       MAE     R^2    Score  \\\n",
       "0           XGBoost           1.278   15388.65   7198.20  0.9616  0.05123   \n",
       "1  GradientBoosting           8.185   14971.76   7991.69  0.9637  0.05161   \n",
       "2          LightGBM           0.292   16667.83   8054.88  0.9550  0.05376   \n",
       "3      RandomForest          10.826   21910.33  12267.13  0.9222  0.08547   \n",
       "4        KNeighbors           0.002   31176.21  14677.41  0.8426  0.09667   \n",
       "5  LinearRegression           0.033   88167.05  17192.36 -0.2592  0.13401   \n",
       "6             Ridge           0.003   88184.92  17186.57 -0.2597  0.13401   \n",
       "7        ElasticNet           0.179  105148.23  18837.56 -0.7910  0.14246   \n",
       "8          AdaBoost           2.770   31977.20  21568.23  0.8344  0.15272   \n",
       "9             Lasso           0.010  247505.43  34986.20 -8.9232  0.21209   \n",
       "\n",
       "   Rank_add_self  Rank_leaderboard  \n",
       "0             79                79  \n",
       "1             80                79  \n",
       "2             81                79  \n",
       "3             98                95  \n",
       "4            105               101  \n",
       "5           1629              1624  \n",
       "6           1629              1624  \n",
       "7           2339              2333  \n",
       "8           3287              3280  \n",
       "9           4098              4091  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['Type','Hyperparameters'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a12fef79",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family']='Times New Roman'\n",
    "plt.style.use('ggplot')\n",
    "colors = plt.cm.viridis_r(np.linspace(0, 0.6, len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1de7b142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.bar(df['Model'],df['Train Time (s)'],color=colors)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Train Time (s)')\n",
    "plt.title('Train Time Comparison')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af59d9d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "431fd761",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5390574",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2fc9877c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zengx\\AppData\\Local\\Temp\\ipykernel_21572\\1987318195.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='RMSE', y='Model', data=best_models_df_rmse_sorted, palette=performance_colors_reversed, ax=axes[0])\n",
      "C:\\Users\\zengx\\AppData\\Local\\Temp\\ipykernel_21572\\1987318195.py:46: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='MAE', y='Model', data=best_models_df_rmse_sorted, palette=performance_colors_reversed, ax=axes[1])\n",
      "C:\\Users\\zengx\\AppData\\Local\\Temp\\ipykernel_21572\\1987318195.py:54: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='R^2', y='Model', data=best_models_df_r2_sorted, palette=performance_colors, ax=axes[2]) # R^2: higher is better, so use normal palette\n",
      "C:\\Users\\zengx\\AppData\\Local\\Temp\\ipykernel_21572\\1987318195.py:62: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='Train Time (s)', y='Model', data=best_models_df_time_sorted, palette=efficiency_colors, ax=axes[3])\n",
      "C:\\Users\\zengx\\AppData\\Local\\Temp\\ipykernel_21572\\1987318195.py:70: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='Score', y='Model', data=best_models_df_score_sorted, palette=submission_colors, ax=axes[4])\n",
      "C:\\Users\\zengx\\AppData\\Local\\Temp\\ipykernel_21572\\1987318195.py:79: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='Rank_leaderboard', y='Model', data=best_models_df_rank_sorted, palette=submission_colors, ax=axes[5])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数下所有模型性能对比图已保存至: ./comparison_results\\best_tuned_models_performance_comparison.png\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "best_models_df = pd.read_csv(r\"E:\\机器学习房价预测\\高分回归\\comparison_results\\rank_and_score.csv\")\n",
    "\n",
    "# Sort by RMSE for performance metrics, and by Train Time for efficiency\n",
    "best_models_df_rmse_sorted = best_models_df.sort_values(by='RMSE', ascending=True)\n",
    "best_models_df_time_sorted = best_models_df.sort_values(by='Train Time (s)', ascending=True)\n",
    "best_models_df_r2_sorted = best_models_df.sort_values(by='R^2', ascending=False)\n",
    "best_models_df_score_sorted = best_models_df.sort_values(by='Score', ascending=True) # Score: lower is better\n",
    "best_models_df_rank_sorted = best_models_df.sort_values(by='Rank_leaderboard', ascending=True) # Rank: lower is better\n",
    "\n",
    "# 设置绘图美学风格\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Times New Roman'] # For Chinese characters\n",
    "plt.rcParams['axes.unicode_minus'] = False # For displaying minus sign correctly\n",
    "\n",
    "# Create a figure with 3 rows and 2 columns for all metrics\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(20, 20))\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Define a color palette for performance metrics (lower RMSE/MAE, higher R^2 are \"better\")\n",
    "# For RMSE/MAE, lower values are better, so color them from good (green) to bad (red)\n",
    "# For R^2, higher values are better, so color them from good (green) to bad (red)\n",
    "performance_colors = sns.color_palette(\"viridis\", n_colors=len(best_models_df))\n",
    "performance_colors_reversed = performance_colors[::-1] # To make lower RMSE/MAE models stand out\n",
    "\n",
    "# Define colors for training time and submission metrics (lower is better)\n",
    "efficiency_colors = sns.color_palette(\"magma\", n_colors=len(best_models_df))\n",
    "submission_colors = sns.color_palette(\"plasma\", n_colors=len(best_models_df))\n",
    "\n",
    "\n",
    "# Plot 1: RMSE Comparison\n",
    "sns.barplot(x='RMSE', y='Model', data=best_models_df_rmse_sorted, palette=performance_colors_reversed, ax=axes[0])\n",
    "axes[0].set_title('最佳参数下各模型性能 - RMSE (越低越好)', fontsize=16)\n",
    "axes[0].set_xlabel('RMSE (美元)', fontsize=12)\n",
    "axes[0].set_ylabel('模型', fontsize=12)\n",
    "\n",
    "for i, (rmse_val, model_name) in enumerate(zip(best_models_df_rmse_sorted['RMSE'], best_models_df_rmse_sorted['Model'])):\n",
    "    axes[0].text(rmse_val + (best_models_df_rmse_sorted['RMSE'].max() * 0.005), i, f'{rmse_val:.2f}', va='center', fontsize=10, color='black')\n",
    "\n",
    "# Plot 2: MAE Comparison\n",
    "sns.barplot(x='MAE', y='Model', data=best_models_df_rmse_sorted, palette=performance_colors_reversed, ax=axes[1])\n",
    "axes[1].set_title('最佳参数下各模型性能 - MAE (越低越好)', fontsize=16)\n",
    "axes[1].set_xlabel('MAE (美元)', fontsize=12)\n",
    "axes[1].set_ylabel('模型', fontsize=12)\n",
    "for i, (mae_val, model_name) in enumerate(zip(best_models_df_rmse_sorted['MAE'], best_models_df_rmse_sorted['Model'])):\n",
    "    axes[1].text(mae_val + (best_models_df_rmse_sorted['MAE'].max() * 0.005), i, f'{mae_val:.2f}', va='center', fontsize=10, color='black')\n",
    "\n",
    "# Plot 3: R^2 Comparison\n",
    "sns.barplot(x='R^2', y='Model', data=best_models_df_r2_sorted, palette=performance_colors, ax=axes[2]) # R^2: higher is better, so use normal palette\n",
    "axes[2].set_title('最佳参数下各模型性能 - R^2 (越高越好)', fontsize=16)\n",
    "axes[2].set_xlabel('R^2', fontsize=12)\n",
    "axes[2].set_ylabel('模型', fontsize=12)\n",
    "for i, (r2_val, model_name) in enumerate(zip(best_models_df_r2_sorted['R^2'], best_models_df_r2_sorted['Model'])):\n",
    "    axes[2].text(r2_val + (best_models_df_r2_sorted['R^2'].max() * 0.005), i, f'{r2_val:.4f}', va='center', fontsize=10, color='black')\n",
    "\n",
    "# Plot 4: Train Time Comparison\n",
    "sns.barplot(x='Train Time (s)', y='Model', data=best_models_df_time_sorted, palette=efficiency_colors, ax=axes[3])\n",
    "axes[3].set_title('最佳参数下各模型训练时间 (秒) (越低越好)', fontsize=16)\n",
    "axes[3].set_xlabel('训练时间 (秒)', fontsize=12)\n",
    "axes[3].set_ylabel('模型', fontsize=12)\n",
    "for i, (time_val, model_name) in enumerate(zip(best_models_df_time_sorted['Train Time (s)'], best_models_df_time_sorted['Model'])):\n",
    "    axes[3].text(time_val + (best_models_df_time_sorted['Train Time (s)'].max() * 0.005), i, f'{time_val:.3f}', va='center', fontsize=10, color='black')\n",
    "\n",
    "# Plot 5: Submission Score Comparison\n",
    "sns.barplot(x='Score', y='Model', data=best_models_df_score_sorted, palette=submission_colors, ax=axes[4])\n",
    "axes[4].set_title('Kaggle 提交得分 (越低越好)', fontsize=16)\n",
    "axes[4].set_xlabel('Kaggle Score', fontsize=14)\n",
    "axes[4].set_ylabel('模型', fontsize=12)\n",
    "for i, (score_val, model_name) in enumerate(zip(best_models_df_score_sorted['Score'], best_models_df_score_sorted['Model'])):\n",
    "    axes[4].text(score_val + (best_models_df_score_sorted['Score'].max() * 0.005), i, f'{score_val:.5f}', va='center', fontsize=10, color='black')\n",
    "\n",
    "\n",
    "# Plot 6: Leaderboard Rank Comparison\n",
    "sns.barplot(x='Rank_leaderboard', y='Model', data=best_models_df_rank_sorted, palette=submission_colors, ax=axes[5])\n",
    "axes[5].set_title('Kaggle 排行榜排名 (越低越好)', fontsize=16)\n",
    "axes[5].set_xlabel('Kaggle Rank', fontsize=14)\n",
    "axes[5].set_ylabel('模型', fontsize=12)\n",
    "for i, (rank_val, model_name) in enumerate(zip(best_models_df_rank_sorted['Rank_leaderboard'], best_models_df_rank_sorted['Model'])):\n",
    "    axes[5].text(rank_val + (best_models_df_rank_sorted['Rank_leaderboard'].max() * 0.005), i, f'{int(rank_val)}', va='center', fontsize=10, color='black')\n",
    "\n",
    "\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "plt.suptitle('最佳参数下所有模型性能对比', y=1.00, fontsize=20, weight='bold') # Overall title\n",
    "plt.show()\n",
    "\n",
    "# Save the figure\n",
    "plot_output_dir = \"./comparison_results\"\n",
    "os.makedirs(plot_output_dir, exist_ok=True)\n",
    "plot_output_path = os.path.join(plot_output_dir, \"best_tuned_models_performance_comparison.png\")\n",
    "fig.savefig(plot_output_path, dpi=300, bbox_inches='tight')\n",
    "print(f\"最佳参数下所有模型性能对比图已保存至: {plot_output_path}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdbfd871",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zengx\\AppData\\Local\\Temp\\ipykernel_21572\\849033993.py:60: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='Weighted_Score', y='Model', data=normalized_df, palette=colors)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加权综合评价得分对比图已保存至: ./comparison_results\\weighted_model_performance_comparison.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "\n",
    "df_metrics = pd.read_csv(r\"E:\\机器学习房价预测\\高分回归\\comparison_results\\rank_and_score.csv\")\n",
    "\n",
    "# Define columns to normalize and their \"direction\" (lower is better vs. higher is better)\n",
    "lower_is_better_cols = ['RMSE', 'MAE', 'Train Time (s)', 'Score', 'Rank_leaderboard']\n",
    "higher_is_better_cols = ['R^2']\n",
    "\n",
    "# Normalize metrics to a 0-1 scale\n",
    "normalized_df = df_metrics[['Model']].copy()\n",
    "\n",
    "for col in lower_is_better_cols:\n",
    "    min_val = df_metrics[col].min()\n",
    "    max_val = df_metrics[col].max()\n",
    "    if (max_val - min_val) == 0: # Handle cases where all values are the same\n",
    "        normalized_df[f'Normalized_{col}'] = 0.5\n",
    "    else:\n",
    "        normalized_df[f'Normalized_{col}'] = (max_val - df_metrics[col]) / (max_val - min_val)\n",
    "\n",
    "for col in higher_is_better_cols:\n",
    "    min_val = df_metrics[col].min()\n",
    "    max_val = df_metrics[col].max()\n",
    "    if (max_val - min_val) == 0: # Handle cases where all values are the same\n",
    "        normalized_df[f'Normalized_{col}'] = 0.5\n",
    "    else:\n",
    "        normalized_df[f'Normalized_{col}'] = (df_metrics[col] - min_val) / (max_val - min_val)\n",
    "\n",
    "# Define weights for each normalized metric (adjust as needed)\n",
    "# Sum of weights should ideally be 1 for easy interpretation, but can be scaled later.\n",
    "weights = {\n",
    "    'Normalized_RMSE': 0.15,\n",
    "    'Normalized_MAE': 0.15,\n",
    "    'Normalized_R^2': 0.15,\n",
    "    'Normalized_Train Time (s)': 0.15,\n",
    "    'Normalized_Score': 0.20,\n",
    "    'Normalized_Rank_leaderboard': 0.20\n",
    "}\n",
    "\n",
    "# Calculate weighted total score\n",
    "normalized_df['Weighted_Score'] = 0\n",
    "for col, weight in weights.items():\n",
    "    normalized_df['Weighted_Score'] += normalized_df[col] * weight\n",
    "\n",
    "# Sort by Weighted_Score in descending order (higher score is better)\n",
    "normalized_df = normalized_df.sort_values(by='Weighted_Score', ascending=False).reset_index(drop=True)\n",
    "\n",
    "# Set plotting style\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS'] # For Chinese characters\n",
    "plt.rcParams['axes.unicode_minus'] = False # For displaying minus sign correctly\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "colors = sns.color_palette(\"viridis\", n_colors=len(normalized_df))\n",
    "sns.barplot(x='Weighted_Score', y='Model', data=normalized_df, palette=colors)\n",
    "\n",
    "plt.title('各模型加权综合评价得分', fontsize=18, weight='bold')\n",
    "plt.xlabel('加权综合得分 (越高越好)', fontsize=14)\n",
    "plt.ylabel('模型', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Add value labels to the bars\n",
    "for index, row in normalized_df.iterrows():\n",
    "    plt.text(row['Weighted_Score'] + 0.005, index, f'{row[\"Weighted_Score\"]:.3f}', va='center', fontsize=11, color='black')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure\n",
    "plot_output_dir = \"./comparison_results\"\n",
    "os.makedirs(plot_output_dir, exist_ok=True)\n",
    "plot_output_path = os.path.join(plot_output_dir, \"weighted_model_performance_comparison.png\")\n",
    "plt.savefig(plot_output_path, dpi=300, bbox_inches='tight')\n",
    "print(f\"加权综合评价得分对比图已保存至: {plot_output_path}\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  }
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